





import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple

import torch
from audiocraft.models import MusicGen

from transformers import (
    AutoFeatureExtractor,
    AutoTokenizer,
    EncodecModel,
    MusicgenDecoderConfig,
    MusicgenForConditionalGeneration,
    MusicgenProcessor,
    TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging


logging.set_verbosity_info()
_A					:				str				=			logging.get_logger(__name__)


_A					:				Optional[Any]				=			["""model.decoder.embed_positions.weights"""]


def 							__snake_case							(    lowerCAmelCase_	)				->						Any:
    if "emb" in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''emb''' ,							'''model.decoder.embed_tokens'''	)
    if "transformer" in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''transformer''' ,							'''model.decoder'''	)
    if "cross_attention" in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''cross_attention''' ,							'''encoder_attn'''	)
    if "linear1" in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''linear1''' ,							'''fc1'''	)
    if "linear2" in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''linear2''' ,							'''fc2'''	)
    if "norm1" in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''norm1''' ,							'''self_attn_layer_norm'''	)
    if "norm_cross" in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''norm_cross''' ,							'''encoder_attn_layer_norm'''	)
    if "norm2" in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''norm2''' ,							'''final_layer_norm'''	)
    if "out_norm" in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''out_norm''' ,							'''model.decoder.layer_norm'''	)
    if "linears" in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''linears''' ,							'''lm_heads'''	)
    if "condition_provider.conditioners.description.output_proj" in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''condition_provider.conditioners.description.output_proj''' ,							'''enc_to_dec_proj'''	)
    return name


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Tuple[Dict, Dict]:
    SCREAMING_SNAKE_CASE__											=			list(state_dict.keys()	)
    SCREAMING_SNAKE_CASE__											=			{}
    for key in keys:
        SCREAMING_SNAKE_CASE__											=			state_dict.pop(lowerCAmelCase_	)
        SCREAMING_SNAKE_CASE__											=			rename_keys(lowerCAmelCase_	)
        if "in_proj_weight" in key:
            # split fused qkv proj
            SCREAMING_SNAKE_CASE__											=			val[:hidden_size, :]
            SCREAMING_SNAKE_CASE__											=			val[hidden_size : 2 * hidden_size, :]
            SCREAMING_SNAKE_CASE__											=			val[-hidden_size:, :]
        elif "enc_to_dec_proj" in key:
            SCREAMING_SNAKE_CASE__											=			val
        else:
            SCREAMING_SNAKE_CASE__											=			val
    return state_dict, enc_dec_proj_state_dict


def 							__snake_case							(    lowerCAmelCase_	)				->						MusicgenDecoderConfig:
    if checkpoint == "small":
        # default config values
        SCREAMING_SNAKE_CASE__											=			1_0_2_4
        SCREAMING_SNAKE_CASE__											=			2_4
        SCREAMING_SNAKE_CASE__											=			1_6
    elif checkpoint == "medium":
        SCREAMING_SNAKE_CASE__											=			1_5_3_6
        SCREAMING_SNAKE_CASE__											=			4_8
        SCREAMING_SNAKE_CASE__											=			2_4
    elif checkpoint == "large":
        SCREAMING_SNAKE_CASE__											=			2_0_4_8
        SCREAMING_SNAKE_CASE__											=			4_8
        SCREAMING_SNAKE_CASE__											=			3_2
    else:
        raise ValueError(f'''Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'''	)
    SCREAMING_SNAKE_CASE__											=			MusicgenDecoderConfig(
        hidden_size=lowerCAmelCase_ ,							ffn_dim=hidden_size * 4 ,							num_hidden_layers=lowerCAmelCase_ ,							num_attention_heads=lowerCAmelCase_ ,							)
    return config


@torch.no_grad()
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_=None ,							lowerCAmelCase_=None ,							lowerCAmelCase_="cpu"	)				->						Union[str, Any]:
    SCREAMING_SNAKE_CASE__											=			MusicGen.get_pretrained(lowerCAmelCase_ ,							device=lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			decoder_config_from_checkpoint(lowerCAmelCase_	)

    SCREAMING_SNAKE_CASE__											=			fairseq_model.lm.state_dict()
    SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			rename_state_dict(
        lowerCAmelCase_ ,							hidden_size=decoder_config.hidden_size	)

    SCREAMING_SNAKE_CASE__											=			TaEncoderModel.from_pretrained('''t5-base'''	)
    SCREAMING_SNAKE_CASE__											=			EncodecModel.from_pretrained('''facebook/encodec_32khz'''	)
    SCREAMING_SNAKE_CASE__											=			MusicgenForCausalLM(lowerCAmelCase_	).eval()

    # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
    SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			decoder.load_state_dict(lowerCAmelCase_ ,							strict=lowerCAmelCase_	)

    for key in missing_keys.copy():
        if key.startswith(('''text_encoder''', '''audio_encoder''')	) or key in EXPECTED_MISSING_KEYS:
            missing_keys.remove(lowerCAmelCase_	)

    if len(lowerCAmelCase_	) > 0:
        raise ValueError(f'''Missing key(s) in state_dict: {missing_keys}'''	)

    if len(lowerCAmelCase_	) > 0:
        raise ValueError(f'''Unexpected key(s) in state_dict: {unexpected_keys}'''	)

    # init the composite model
    SCREAMING_SNAKE_CASE__											=			MusicgenForConditionalGeneration(text_encoder=lowerCAmelCase_ ,							audio_encoder=lowerCAmelCase_ ,							decoder=lowerCAmelCase_	)

    # load the pre-trained enc-dec projection (from the decoder state dict)
    model.enc_to_dec_proj.load_state_dict(lowerCAmelCase_	)

    # check we can do a forward pass
    SCREAMING_SNAKE_CASE__											=			torch.arange(0 ,							8 ,							dtype=torch.long	).reshape(2 ,							-1	)
    SCREAMING_SNAKE_CASE__											=			input_ids.reshape(2 * 4 ,							-1	)

    with torch.no_grad():
        SCREAMING_SNAKE_CASE__											=			model(input_ids=lowerCAmelCase_ ,							decoder_input_ids=lowerCAmelCase_	).logits

    if logits.shape != (8, 1, 2_0_4_8):
        raise ValueError('''Incorrect shape for logits'''	)

    # now construct the processor
    SCREAMING_SNAKE_CASE__											=			AutoTokenizer.from_pretrained('''t5-base'''	)
    SCREAMING_SNAKE_CASE__											=			AutoFeatureExtractor.from_pretrained('''facebook/encodec_32khz''' ,							padding_side='''left'''	)

    SCREAMING_SNAKE_CASE__											=			MusicgenProcessor(feature_extractor=lowerCAmelCase_ ,							tokenizer=lowerCAmelCase_	)

    # set the appropriate bos/pad token ids
    SCREAMING_SNAKE_CASE__											=			2_0_4_8
    SCREAMING_SNAKE_CASE__											=			2_0_4_8

    # set other default generation config params
    SCREAMING_SNAKE_CASE__											=			int(3_0 * audio_encoder.config.frame_rate	)
    SCREAMING_SNAKE_CASE__											=			True
    SCREAMING_SNAKE_CASE__											=			3.0

    if pytorch_dump_folder is not None:
        Path(lowerCAmelCase_	).mkdir(exist_ok=lowerCAmelCase_	)
        logger.info(f'''Saving model {checkpoint} to {pytorch_dump_folder}'''	)
        model.save_pretrained(lowerCAmelCase_	)
        processor.save_pretrained(lowerCAmelCase_	)

    if repo_id:
        logger.info(f'''Pushing model {checkpoint} to {repo_id}'''	)
        model.push_to_hub(lowerCAmelCase_	)
        processor.push_to_hub(lowerCAmelCase_	)


if __name__ == "__main__":
  _A					:				int				=			argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--checkpoint""",
      default="""small""",
      type=str,
      help="""Checkpoint size of the MusicGen model you'd like to convert. Can be one of: `['small', 'medium', 'large']`.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder""",
      required=True,
      default=None,
      type=str,
      help="""Path to the output PyTorch model directory.""",
  )
  parser.add_argument(
      """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
  )
  parser.add_argument(
      """--device""", default="""cpu""", type=str, help="""Torch device to run the conversion, either cpu or cuda."""
  )

  _A					:				int				=			parser.parse_args()
  convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)


import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


lowercase    :   List[str]						=			logging.get_logger(__name__)




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def __init__(   self						:Dict	,  *a						:Tuple	,  **a						:List[Any]		)						->					None:
								warnings.warn(
								    "The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
								    " Please use PerceiverImageProcessor instead."	,  a	,  )
								super().__init__(*a	,  **a		)